You Don't Need Public Tests to Generate Correct Code

This paper presents DryRUN, a novel framework for autonomous code generation that eliminates the need for human-authored public test cases. By leveraging LLM's internal capabilities for planning, input synthesis, and mental trace simulation, DryRUN mitigates the 'Overconfidence Gap' often seen in test-dependent methods. It achieves performance comparable to state-of-the-art while operating under zero-example constraints and reducing token usage.

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Executive Impact Summary

DryRUN represents a paradigm shift in AI-driven code generation, offering robust solutions without the traditional bottleneck of human-authored public tests. Its ability to self-correct through mental simulation leads to more reliable and efficient code, directly impacting development cycles and reducing costs.

0 Performance Gain (vs. CodeSIM)

0 Token Reduction (vs. CodeSIM)

Zero Public Test Dependency (Examples)

Deep Analysis & Enterprise Applications

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Methodology

Performance

Impact

Challenges

Enterprise Process Flow

Standardized Spec (Zero Examples)

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Start Loop

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Plan & Generate Initial Code

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Autonomously Synthesize Input

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Mental Trace & Refine (No Execution)

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Final Code

The DryRUN framework introduces a novel approach to code generation by replacing reliance on human-authored public tests with autonomous input synthesis and mental simulation. This shift allows LLMs to self-correct without external execution, making the process more adaptable to real-world scenarios where ground-truth examples are scarce. This also helps in reducing the 'Overconfidence Gap' which leads to overfitting on trivial test cases.

Method	Easy	Med	Hard	Overall
CodeSIM Requires Public Tests External Execution Higher Overconfidence Gap	92.6%	77.3%	41.4%	64.2%
DryRUN (Ours) Zero Examples Mental Simulation Only Lower Overconfidence Gap	90.7%	72.0%	53.2%	67.5%

Lower Overconfidence Gap (Avg. -30%)

Addressing the 'Overconfidence Gap'

Traditional LLM-based code generation methods often overfit to simple public tests, leading to failures on complex hidden test suites. This 'Overconfidence Gap' is a major challenge in deploying reliable AI-generated code.

DryRUN tackles this by forcing the LLM to validate logic against its own non-trivial, synthesized inputs. The iterative mental simulation and refinement process ensures robust self-correction, minimizing reliance on potentially trivial external oracles.

Empirical results show that DryRUN substantially mitigates the Overconfidence Gap, leading to more resilient code that performs consistently across diverse test scenarios, including hidden private suites.

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Annual Cost Savings $0

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Your AI Code Generation Roadmap

A structured approach to integrating DryRUN's zero-example code generation into your development workflow.

Phase 1: Zero-Example Specification

Standardize problem specifications to remove human-authored examples, preparing for autonomous input synthesis.

Phase 2: Autonomous Planning & Refinement

Integrate LLM-driven iterative planning and self-correction, reducing initial logical oversights before code generation.

Phase 3: Mental Simulation & Debugging

Deploy DryRUN's core loop: autonomous input synthesis, mental trace simulation, and trace-driven refinement without external execution.

Phase 4: Final Code Polishing & Deployment

Apply a final polishing stage to resolve syntax and stylistic anomalies, ensuring production-ready, self-corrected solutions.

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You Don't Need Public Tests to Generate Correct Code